International Business Machines Corporation

David Ocheltree

Frederik Frank Flöther

Patrick Lustenberger

Andrea Giovannini

Stefan Ravizza

Saurabh Yadav

Matthias Biniok

A processor may gather raw data comprising a plurality of characteristic data samples of a target user group. The processor may categorize the characteristic data samples into a plurality of user-related classes and triggers. The processor may build an input property graph for each characteristic data sample. The processor may augment the input property graph by a concept of hierarchies. The processor may determine a modification vector from the augmented input property graph. The processor may train an encoder/decoder combination machine-learning system. An embedding vector and a modification vector are used as input for the decoder to build a trained machine-learning generative model.

Modification and generation of conditional data

In an approach, a processor creates a multi-layered knowledge graph (KG), wherein a first layer is a core KG, a second layer has application-specific structured facts, and a third layer has individualized facts. A processor adapts weights in each layer of the multi-layered KG based on the individualized facts. A processor uses, as input data to the multi-layered KG, individual environmental data. A processor maps the input data to the multi-layered KG in a sequence of the first layer, the second layer, and the third layer. A processor selects, as relevant nodes in the first layer and the second layer, the relevant nodes lying on a selected path from the input data via the first layer, the second layer, and the third layer having the highest average weight value along the selected path. A processor outputs facts of the relevant nodes from the first layer and the second layer.

Environment augmentation based on individualized knowledge graphs

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Peachtree City, GA, United States of America

David Ocheltree